Unveiling higher education students’ experiences of using artificial intelligence: a cross-institutional qualitative study unveiling higher education students’ experiences of using artificial intelligence: a cross-institutional qualitative study

Abstract

Higher Education (HE) has yet to fully embrace the potential of artificial intelligence (AI), likely due to lack of funding, a general reticence to take risks or adopt innovations, limited empirical research and theoretical groundings, together with an emerging understanding of the role of such technology in HE (Wheeler, 2019; McGrath et al., 2024). Lack of digital literacy (such as AI literacy) among educators and students also poses a significant barrier (Lincoln and Kearney, 2019 cited in Essien, Bukoye, O’Dea & Kremantzis, 2024; Mah & Groß, 2024; Tully et al., 2025). Those who use AI in education may fail to recognise the constructivist and developmental nature of learning, imposing instead behaviourism-based teaching methods and an objectivist epistemology (Bates et al., 2020). Research on AI in education is developing as AI technology evolves (McGrath et al., 2024). There is a tendency to focus on the negative implications of AI in learning and teaching, but there are calls for greater consideration of its strengths (Bates et al., 2020). Research tends to favour positivist paradigms (Budhathoki et al., 2024; Zhao et al., 2024) over understanding students’ subjective experiences of engaging with AI, which offers important insights into its potential impact in enhancing and hindering learning. Consequently, a team of researchers from four UK-based HE institutions are exploring students’ experiences of using AI in their studies. Following delivery of an learning development themed AI workshop, used partly as a recruitment strategy, we are using a qualitative approach that allows for sensitivity to the social processes in which experiences are embedded (Creswell, 2009). Thematic analysis will give rise to themes that capture how students are using AI, possible barriers to accessing it, and affective dimensions that may hinder/facilitate engagement. By sharing these themes, we hope to provide a more granular perspective, unearthing nuanced and authentic insights from students from multiple institutions into how they are (or are not) using AI. The findings will have implications for how learning developers can best support the use of AI to enhance learning while addressing accessibility, inclusivity, and affective considerations.